IoT Device Labeling Using Large Language Models
arxiv(2024)
摘要
The IoT market is diverse and characterized by a multitude of vendors that
support different device functions (e.g., speaker, camera, vacuum cleaner,
etc.). Within this market, IoT security and observability systems use real-time
identification techniques to manage these devices effectively. Most existing
IoT identification solutions employ machine learning techniques that assume the
IoT device, labeled by both its vendor and function, was observed during their
training phase. We tackle a key challenge in IoT labeling: how can an AI
solution label an IoT device that has never been seen before and whose label is
unknown?
Our solution extracts textual features such as domain names and hostnames
from network traffic, and then enriches these features using Google search data
alongside catalog of vendors and device functions. The solution also integrates
an auto-update mechanism that uses Large Language Models (LLMs) to update these
catalogs with emerging device types. Based on the information gathered, the
device's vendor is identified through string matching with the enriched
features. The function is then deduced by LLMs and zero-shot classification
from a predefined catalog of IoT functions.
In an evaluation of our solution on 97 unique IoT devices, our function
labeling approach achieved HIT1 and HIT2 scores of 0.7 and 0.77, respectively.
As far as we know, this is the first research to tackle AI-automated IoT
labeling.
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